The European Union (EU)-funded SENSEI project accurately anticipated Britain’s Brexit decision based on an analysis of more than 6 million social-media conversations in the weeks leading up to the vote, according to project coordinator Giuseppe Riccardi. “It appears that the momentum on U.K. social media started to change on June 21 [two days before the referendum vote] and we watched it move,” he says. Traditional pollsters predicted a vote to stay in the EU would narrowly prevail. Using a combination of human intervention and machine-reading algorithms, SENSEI estimated the tenor of U.K.-based social-media conversations made a vote too close to call on June 23. However, by late afternoon, an analysis of online dialogues led to a prediction of 48 percent of U.K. voters voting to stay and 52 percent voting to leave, which was precisely reflected in the final referendum outcome. “This is a great result for the project,” says SENSEI’s Hugo Zaragoza. “The ability to listen to millions of pieces of conversations and then analyzing them for sentiment, using a combination of humans and machines, has proved…to be more successful than traditional polling methods.” He says the project offers a highly valuable commercial tool to help political and business commentators understand what is being said online.